In the post-Moore's Law era, relying solely on hardware advancements for automatic performance gains is no longer feasible without increased energy consumption, due to the end of Dennard scaling. Consequently, computing accounts for an increasing amount of global energy usage, contradicting the objective of sustainable computing. The lack of hardware support and the absence of a standardized, software-centric method for the precise tracing of energy provenance exacerbates the issue. Aiming to overcome this challenge, we argue that fine-grained software energy attribution is attainable, even with limited hardware support. To support our position, we present a thread-level, NUMA-aware energy attribution method for CPU and DRAM in multi-tenant environments. The evaluation of our prototype implementation, EnergAt, demonstrates the validity, effectiveness, and robustness of our theoretical model, even in the presence of the noisy-neighbor effect. We envisage a sustainable cloud environment and emphasize the importance of collective efforts to improve software energy efficiency.

翻译：在后摩尔定律时代，由于登纳德缩放定律的终结，仅依靠硬件进步自动提升性能而不增加能耗已不再可行。因此，计算在全球能耗中的占比持续上升，这与可持续计算的愿景相悖。硬件支持的匮乏以及缺乏标准化、软件主导的精确能耗溯源方法进一步加剧了这一问题。为应对这一挑战，我们认为即便在有限硬件支持下，仍可实现细粒度的软件能耗归因。为此，我们提出了一种支持线程级与NUMA感知的多租户环境下CPU及DRAM能耗归因方法。原型系统EnergAt的评估验证了理论模型的有效性、鲁棒性与准确性，即便在存在"噪声邻居"效应时依然表现稳定。我们展望可持续云环境的前景，并强调集体努力提升软件能效的重要性。